Generate API Documentation from Your Codebase

API documentation is always wrong

You know the pattern. Someone writes API docs when the backend launches. Three sprints later, two endpoints have changed, a new query parameter exists, and the response shape for /tags has an extra nested object that nobody documented. The wiki page still says v1. The Postman collection is from October. The frontend team is reverse-engineering your controllers to figure out what the API actually returns.

This is not a tooling problem. It is a freshness problem. Documentation written by hand decays the moment the code changes. And no team has the discipline to update docs every time they modify a route handler or add a field to an interface. The result: your frontend developers are reading your backend code directly, asking questions in Slack, or — worse — shipping integrations based on assumptions that turn out to be wrong.

How a developer documented a NestJS backend in minutes

A developer needed to understand how labels and categories flow from a NestJS backend to a mobile app. They asked the agent: “Tell me how we send labels and categories to the frontend — what controller, what route, what interfaces.” The agent cloned the backend repository, found the TrendAgents controller, read the ITag interface definition, and produced a complete API contract — endpoints, parameters, response types, and data flow.

No manual code reading. No grep-ing through files. The agent traced the actual code path from controller to interface to response shape and documented every step. The developer got a structured contract showing the route, the HTTP method, the expected parameters, and the TypeScript interface defining the response body. All grounded in the source code that was running in production.

From documentation to GitHub issue in one conversation

The same developer then said: “Prepare API contracts for the /tags route that allow us to implement new logic on the frontend.” The agent generated a structured API contract document and the developer attached it directly to a GitHub issue for the mobile team. The frontend developers never needed to read the backend code.

This is the workflow that makes API documentation useful. The contract goes into the issue tracker where the implementing team actually works. Not a wiki page they will never check. Not a Slack message that scrolls away. A GitHub issue with the exact endpoint, parameters, and response types — ready to implement against. LikeClaw’s GitHub integration makes this a single conversation: generate the docs, create the issue, assign the team.

Documenting endpoints from specific files

Another developer fetched specific files from a private repo — TrendAgents.controller.ts and model/ITag.ts — and asked the agent to document the endpoint behavior. The agent read the TypeScript code, traced the data flow from the controller method through the service layer to the interface definition, and produced documentation that matched the actual implementation.

This approach works when you already know which files matter. Point the agent at the specific controller and model files, and it produces targeted documentation. No need to clone the entire repo if you only need docs for one endpoint group. The agent reads the code you give it and documents what it finds — decorators, route parameters, middleware, return types, and error handling patterns.

Why sandboxed repo access matters for documentation

When you grant an AI agent access to your private repository, where that code runs matters. The agent needs to clone your repo, read your source files, and potentially execute build tools to resolve types and dependencies. If that happens on your local machine with raw system access, you are exposing your entire development environment to whatever the agent does.

Snyk’s research found 341+ malicious packages in open-source AI tool registries. LikeClaw runs every documentation task in an E2B sandboxed container. Your repo is cloned inside the sandbox. The agent reads the code inside the sandbox. When the task is done, the sandbox is destroyed. Your local machine, your SSH keys, your other repositories — none of it is exposed.

Your credentials are encrypted and scoped to the sandbox session. Zero lateral movement between tasks. Zero persistence after completion.

Beyond single-endpoint docs

The real power shows up when you need to document APIs across an entire backend or across multiple services. Common patterns from beta users:

• Full API inventory: “List every endpoint in this Express app with its method, path, and handler function.” The agent walks the route tree and produces a complete inventory.
• Cross-service contracts: Point the agent at your API gateway and two downstream services. It traces how a request flows through each layer and documents the contract at every boundary.
• Migration documentation: “Compare the v1 and v2 endpoints and document what changed.” The agent reads both versions and produces a diff-style migration guide.
• Schema extraction: “Extract all TypeScript interfaces used in API responses and produce a schema document.” The agent collects every interface referenced in route handlers and outputs them in one structured file.

This pairs naturally with LikeClaw’s codebase analysis capabilities. Analyze the architecture first to understand the system, then generate targeted API documentation for the endpoints that matter. Both tasks run in the same secure sandbox, and results persist in your workspace for reference.

Who this is for

Backend developers tired of answering “What does this endpoint return?” in Slack. Team leads who need to hand off API contracts to frontend or mobile teams without scheduling a meeting. Platform engineers documenting internal APIs for other teams. Anyone who has ever searched a company wiki for API docs and found a page last updated eight months ago.

Zero setup. Predictable pricing. Documentation generated from your actual code, not from memory.